Openflow App Testing

Report
Openflow App Testing
Chao SHI,
Stephen Duraski
Motivation
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Network is still a complex stuff !
Distributed mechanism
o Complex protocol
o Large state
o Asynchronous events, random ordering, race
conditions.
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Motivation
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Openflow does not reduce complexity, it just
encapsulates it.
The distributed and asynchronous feature of
networking is inherent
o The correctness of controller not verified
o Hard to test your apps on Openflow controller
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Race Condition
Background
Finite State Machine
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A model of hardware or computer programs
State
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Variable value assignment, flags, code block in
execution, etc
Transition
Input (Event)
Output (Action)
State Diagram
Model Checking for Distributed
System
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System:
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State:
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Components & Channels
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Component State
System State = Component States + Channel Contents
Transition
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Enabled Transitions (System level)
System Execution (Sequence of Transitions)
Model Checking
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What to look for:
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Method:
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Deadlocks, incompatible states causing crash
Both hardware and software
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State Space Enumeration
 Depth First Search using stack in the paper
No Scalability
Less suitable for software
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Large possibility of user input
"Undecidability cannot be fully algorithmic"
Symbolic Execution
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Tracking the symbolic variable rather than
actual value
Symbolic variable is associated with input
Real variables are associated with symbolic
variables and the expression is updated
accordingly
Fork at every branch.
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Still not scalable
Symbolic Execution
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Equivalent class
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Same code path for all instances in the same class
Still problems
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Code path coverage does not mean system path coverage
Combine State Modeling and Symbolic
Execution is good !
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State modeling to exhaust system states
Symbolic execution to reduce transition numbers
Design
Modeling in Openflow
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State: Controller + Switch + Host
Transition
Input:
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Event (Packet_IN, Flow_Removed, Port_Status)
Message Content (OFMessage) ( We don't want it )
Output:
The actual code path of the event Handler (Atomic)
o That problem can be solved using the extension of
Symbolic Execution
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OFSwitch and Host Simplification
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Switch is channels + flow table
Switch has Two simple transition
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process_pkt, process_of
process_pkt: multiple packet processing - > a state transfer
Equivalent flow table is the same state
Host is categorized
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Server-- receive / send_reply
Client -- send / receive
Symbolic Execution
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Find the equivalent class of input packet
Pick one packet from the class as relevant
packet
Symbolic packet composed of Symbolic
variables
Concrete Controller State
Combination
Initialization: enable
discover_packets by
every switch
Run discover_packets_transition
and enable new transitions
Are you the switch in
discover_packets_transiti
on. If so, your next
transition is already
assigned. If not, continue
listening on packets
state of the ctrl is
changed
OpenFlow Specific Search
Strategies
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PKT-SEQ: Relevant packet sequences
NO-DELAY: Instantaneous rule updates
UNUSUAL: Uncommon delays and
reordings
FLOW-IR: Flow independence reduction
PKT-SEQ is complementary to other
strategies in that it only reduces the number
of send transitions rather than the possible
kind of event orderings
Specifying Application Correctness
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Customizable correctness properties testing correctness involves verifying that
"something bad never happens" (safety) and
that "something good eventually happens"
(liveness).
To check these properties, NICE allows
correctness properties to (i)access the
system state, (ii) register callbacks, and (iii)
maintain local state
Specifying Application Correctness
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NICE provides a library of correctness
properties applicable to a wide variety of
OpenFlow applications.
Includes: NoForwardingLoops,
NoBlackHoles, DirectPaths,
StrictDirectPaths, NoForgottenPackets
Implementation
NICE consists of 3 parts:
A model checker - NICE remembers the sequence of
transitions that created the state and restores it by
replaying the sequence, state-matching is doing by
comparing and storing hashes of the explored states.
A concolic execution engine - executes the Python code
with concrete instead of symbolic inputs
A collection of models including the simplified switch
and several end hosts.
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Performance
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The experiments are run on the topology of Figure 1
(reproduced here)
Host A sends a “layer-2 ping” packet to host B which
replies with a packet to A.
The controller runs the MAC-learning switch program of
Figure 3.
The numbers of transitions and unique states, and the
execution time as the number of concurrent pings is
increased (a pair of packets) are reported.
Performance
Performance
Comparison to state-of-the-art model checkers SPIN and
JPF
SPIN performs a full search more efficiently, but statespace explosion occurs and SPIN runs out of memory
with 7 pings
SPIN's Partial-order reduction only decreases the
growth rate by 18%
JPF is already slower than nice, and even after extra
effort is put into improving the Java model used in JPF,
it is still substantially slower.
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NICE and Real Applications
• NICE detected 3 bugs in a MAC-learning switch
(PySwitch)
o Host unreachable after moving
o Delayed direct path
o Excess flooding
NICE and Real Applications
• NICE found several bugs in a Web Server Load
Balancer (dynamically reconfigures data center
traffic based on load)
o Next TCP packet always dropped after
reconfiguration
o Some TCP packets dropped after
reconfiguration
o ARP packets forgotten during address
resolution
o Duplicate SYN packets during transitions
NICE and Real Applications
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Energy-Efficient Traffic Engineering (selectively
powering down links and redirecting during light
loads)
o First packet of new flow is dropped
o The first few packets of a new flow can be
dropped
o Only on-demand routes used under high
loads
o Packets can be dropped when the load
reduces
Overhead of running NICE
Coverage vs. Overhead Trade-Offs
There are some current limitations to NICE with
the current approach
Concrete execution on the switch
Concrete global controller variables
Infinite execution trees in symbolic execution
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Related work
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NICE uses ideas in model checking and
symbolic execution, and is the first to apply
those techniques to testing OpenFlow
networks.
Other work such as Frenetic, OFRewind,
and FlowChecker can perform limited testing
in OpenFlow networks.

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